Autonomous Driving System Testing via Diversity-Oriented Driving Scenario Exploration

Testing Autonomous Driving Systems (ADS) is critical for validating their safety in operational environments. High-fidelity simulators enable the testing of ADS through virtual driving scenarios, especially those that are hazardous to replicate in real-world settings. However, existing testing approaches suffer from inadequate coverage of real-world traffic situations due to over-simplified modeling of vehicle movements (e.g., insufficient diversity in driving styles), resulting in undetected critical ADS failures. In this paper, we propose a testing framework to discover diverse failures of ADS in driving scenarios that embody real-world traffic complexity. The framework leverages advanced traffic simulation methods to encode vehicle movements and generates realistic yet safety-critical driving scenarios for ADS by mutating vehicle movements. To efficiently explore driving scenarios that pose different challenges for ADS and expose diverse ADS failures, this framework further leverages a dynamic prioritization mechanism that prioritizes vehicle movements likely to trigger unique ADS behaviors. Specifically, we propose a method to estimate the possibility based on encoded vehicle movements. We implement this framework and evaluate it with three representative ADS from the famous CARLA leaderboard. Empirical evaluation demonstrates that the proposed approach discovers more unique failures of ADS than existing testing frameworks.